
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "70"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 80 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 76 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	digital_ok	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	0.691679	12.077528	-0.186888	80.819659	-1.021163	27.900763	-1.307045	32.529026	0.7817	0.0805	0.4920	12.973345	1.210176
2459593	digital_ok	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	-0.301669	18.141478	0.694962	85.724247	-1.073291	37.678068	-0.692729	11.448996	0.7070	0.0744	0.4660	6.449126	1.219183
2459592	digital_ok	100.00%	100.00%	100.00%	0.00%	-	-	0.511687	1.509094	-0.028133	7.398878	-1.274389	2.087865	-1.848515	0.587457	0.0408	0.0315	0.0017	nan	nan
2459591	digital_ok	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	-0.524851	21.285315	0.590984	99.548200	-0.211716	32.180000	-0.196168	2.350415	0.6110	0.0538	0.4239	7.328848	1.247494
2459590	digital_ok	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	-0.305226	24.646138	0.298691	92.780237	-1.627406	34.873459	-0.660638	4.682512	0.6114	0.0576	0.4244	6.215385	1.253672
2459589	digital_ok	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	-0.840364	22.347722	0.192160	85.965394	-0.235130	21.982112	-0.858927	17.021001	0.6847	0.0787	0.4267	5.520191	1.187465
2459588	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	215.483883	215.578289	inf	inf	22.535495	25.392679	21.440174	19.715039	nan	nan	nan	0.000000	0.000000
2459587	digital_ok	100.00%	-	-	-	100.00%	0.00%	-0.515530	22.682835	5.786595	134.239415	-23.333991	-23.858603	-4.675000	-4.393628	nan	nan	nan	0.000000	0.000000
2459586	digital_ok	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	-0.624774	19.965464	-0.717903	32.349231	-1.098370	11.495577	-0.510561	6.768178	0.6186	0.0571	0.4164	6.530931	1.232616
2459585	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.640967	25.299665	-0.550754	10.889751	-0.639190	10.170060	-0.048384	4.668011	0.0338	0.0343	0.0028	0.000000	0.000000
2459584	digital_ok	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	3.428239	26.909698	0.274007	115.094992	2.278354	18.743526	26.591013	27.747660	0.5713	0.0436	0.3798	0.000000	0.000000
2459583	digital_ok	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	-0.517093	20.225965	-0.794884	35.593779	-1.176103	23.302568	1.494293	0.525580	0.6194	0.0512	0.4279	0.000000	0.000000
2459582	digital_ok	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	-0.783156	17.438413	-0.329193	32.365520	-1.387480	28.357605	-0.279936	0.593873	0.6070	0.0431	0.4291	4.438189	1.174636
2459581	digital_ok	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	-0.805000	24.106799	-1.048957	36.509895	-1.020422	22.831308	0.219237	0.655406	0.6263	0.0524	0.4216	5.076330	1.225732
2459580	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459579	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459578	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	-0.337834	1.539714	-0.796908	2.698847	-0.645941	0.568103	1.779894	2.345819	0.0388	0.0344	0.0011	nan	nan
2459577	digital_ok	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	-0.920613	23.265797	-0.955887	34.136300	-0.997094	24.918758	-0.138699	0.636400	0.6222	0.0521	0.4357	4.378061	1.193866
2459576	digital_ok	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	-0.438018	17.964512	-0.761643	27.395313	-0.570719	22.002430	-0.307314	1.400489	0.6351	0.0563	0.3960	7.213787	1.204220
2459575	digital_ok	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	-0.853658	18.738633	-0.819815	27.179235	-0.379004	16.957566	-0.822958	5.906314	0.7163	0.0668	0.4571	6.062972	1.140294
2459574	digital_ok	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	-0.643962	16.983633	-0.957844	26.018500	-0.392309	24.301857	-0.773642	0.856722	0.6380	0.0509	0.3792	4.737023	1.195977
2459573	digital_ok	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	-0.485457	17.550230	-0.595619	44.745359	-0.931941	28.208793	-0.285781	1.846537	0.6329	0.0508	0.3856	6.086715	1.041125
2459572	digital_ok	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	-0.731812	17.655244	-0.754644	26.181738	-1.062208	18.030109	-0.046614	3.088898	0.6326	0.0525	0.3937	5.766889	1.246846
2459571	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	-0.112464	1.234128	-0.721447	2.786199	-1.042545	0.929880	0.220802	2.644745	0.0337	0.0356	0.0012	nan	nan
2459570	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459569	digital_ok	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	-0.762173	21.121130	-0.923157	33.962087	-1.115784	10.393515	-1.076852	8.746010	0.7431	0.1005	0.4257	6.526992	1.227621
2459566	digital_ok	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	-0.839322	17.042457	-0.879780	33.941199	-1.136542	21.553605	-0.489896	0.031536	0.6215	0.0468	0.3752	3.528100	1.161254
2459565	digital_ok	0.00%	-	-	-	93.33%	0.00%	-1.185954	-1.185954	-1.077553	-1.077553	-0.891166	-0.891166	-0.958079	-0.958079	nan	nan	nan	0.000000	0.000000
2459564	digital_ok	0.00%	0.00%	91.96%	0.00%	95.38%	4.62%	1.491114	2.935452	1.571537	2.804450	-0.641229	0.074590	-1.625990	-1.636119	0.6335	0.3264	0.2396	3.891939	3.553320
2459563	digital_ok	100.00%	0.00%	93.46%	0.00%	100.00%	0.00%	5.199254	7.056703	21.114694	23.754075	10.237398	15.287827	-1.394899	-1.741577	0.6225	0.3105	0.2409	6.266171	2.724369
2459562	digital_ok	0.00%	0.00%	94.10%	0.00%	97.30%	2.70%	0.598908	1.811550	0.321395	0.932952	-0.934181	0.215830	-1.022483	-1.058843	0.6294	0.3244	0.2289	3.801997	3.342347
2459561	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	95.14%	1.166057	1.590404	0.828021	1.120917	-0.777290	-0.176845	-1.956699	-1.801464	0.6362	0.6435	0.3334	6.587538	9.059433
2459560	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	95.14%	0.681416	1.210206	0.433323	0.909686	-0.667642	0.295125	-1.341585	-1.423586	0.6154	0.6181	0.3427	4.433379	8.718503
2459559	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	94.62%	1.276068	1.809836	1.004114	1.354278	-0.378199	0.740283	-1.449697	-1.130159	0.6314	0.6372	0.3282	3.325396	7.659300
2459558	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.080372	5.887826	11.363654	11.642713	11.127581	14.301584	-1.719181	-0.882898	0.6295	0.6279	0.3336	3.305481	8.263354
2459557	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0381	0.0410	0.0025	nan	nan
2459556	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.556898	6.316697	11.895787	11.962188	10.175062	12.867178	-2.811779	-2.521987	0.6374	0.6351	0.3259	4.890108	8.050115
2459554	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6209	0.6058	0.3298	nan	nan
2459553	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6276	0.6138	0.3247	nan	nan
2459552	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.331561	6.890907	15.534995	16.571127	16.218662	19.636151	0.304355	0.374378	0.6434	0.6408	0.3309	0.000000	0.000000
2459551	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	94.62%	0.978767	1.432459	0.774394	1.141778	-1.044493	0.319637	-1.748228	-1.304541	0.6328	0.6306	0.3264	3.984176	7.884282
2459550	digital_ok	-	97.53%	97.53%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0514	0.0508	0.0033	nan	nan
2459549	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.001135	5.661030	11.436380	12.152284	11.089011	13.955088	-1.810645	-1.823838	0.6338	0.6351	0.3338	3.017836	8.612274
2459542	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.342353	5.864645	22.669371	24.393076	0.258127	0.919061	-1.713527	-0.312721	0.6945	0.6917	0.2994	2.735990	7.129441
2459541	digital_ok	100.00%	1.34%	1.34%	0.00%	100.00%	0.00%	5.015378	4.834523	14.280369	14.291130	8.821458	11.087964	3.832279	4.444439	0.6957	0.6879	0.2763	4.580169	6.965315
2459540	digital_ok	0.00%	0.00%	0.00%	0.00%	21.88%	78.12%	2.275528	2.427890	2.228265	2.358675	-0.070704	0.741609	-0.911014	-0.445006	0.6999	0.6910	0.2964	3.794176	7.734817
2459536	digital_ok	100.00%	37.06%	37.06%	0.00%	100.00%	0.00%	7.822093	8.652239	19.066822	19.880937	17.221027	19.706689	-2.574068	-1.504656	0.4235	0.4303	0.2090	2.524264	3.366968
2459535	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.690068	1.799507	1.736751	2.053729	-0.619624	0.442724	-1.443042	-1.113844	0.0613	0.0669	0.0108	1.166358	1.174950
2459534	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.888455	1.483486	0.775264	1.236094	-1.108120	-0.360903	-1.100465	-1.028893	0.0691	0.0854	0.0133	1.162055	1.160882
2459533	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	5.643117	6.328057	11.782019	12.423086	9.140689	12.625275	-1.963871	-1.881126	0.0642	0.0830	0.0132	1.180534	1.184757
2459532	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	6.738960	7.266506	15.492576	15.834470	11.965821	15.154569	-1.402699	-1.658705	0.0612	0.0810	0.0146	1.021220	1.019510
2459530	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.890556	1.370841	0.713328	0.952585	-0.944744	0.198731	-1.585813	-0.925327	0.0716	0.0863	0.0169	1.143310	1.146679
2459527	digital_ok	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.454809	7.576286	12.551637	13.201163	24.540906	21.344407	-1.637439	-0.917988	0.9125	0.9376	0.2726	0.000000	0.000000
2459523	digital_ok	100.00%	-	-	-	100.00%	0.00%	6.326849	6.938523	17.104390	18.410510	9.958509	17.244544	-4.769483	-4.533381	nan	nan	nan	7.400965	17.269019
2459522	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.203385	6.031502	15.684180	15.639047	5.716146	14.017198	-2.392641	-1.770782	0.7089	0.6926	0.3113	5.161157	10.352013
2459513	digital_ok	100.00%	100.00%	100.00%	0.00%	-	-	4.724768	4.851002	6.643091	7.835056	2.545713	-1.026173	0.362123	0.420290	0.0389	0.0402	0.0075	nan	nan
2459508	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	6.300704	7.807620	10.054158	10.225660	9.922899	8.562959	8.914283	11.384246	0.0627	0.0825	0.0064	0.000000	0.000000
2459505	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	68.779414	107.375987	106.304816	107.270539	117.793341	77.038732	657.779668	512.488239	0.0069	0.0064	0.0020	1.106594	1.103004

Antenna Season Report Notebook¶

Antenna 70 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 70, Node 4:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 70: 2459603

Antenna 70: 2459602

Antenna 70: 2459598

Antenna 70: 2459597

Antenna 70: 2459596

Antenna 70: 2459595

Antenna 70: 2459594

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Antenna 70: 2459591

Antenna 70: 2459590

Antenna 70: 2459589

Antenna 70: 2459588

Antenna 70: 2459587

Antenna 70: 2459586

Antenna 70: 2459585

Antenna 70: 2459584

Antenna 70: 2459583

Antenna 70: 2459582

Antenna 70: 2459581

Antenna 70: 2459580

Antenna 70: 2459579

Antenna 70: 2459578

Antenna 70: 2459577

Antenna 70: 2459576

Antenna 70: 2459575

Antenna 70: 2459574

Antenna 70: 2459573

Antenna 70: 2459572

Antenna 70: 2459571

Antenna 70: 2459570

Antenna 70: 2459569

Antenna 70: 2459566

Antenna 70: 2459565

Antenna 70: 2459564

Antenna 70: 2459563

Antenna 70: 2459562

Antenna 70: 2459561

Antenna 70: 2459560

Antenna 70: 2459559

Antenna 70: 2459558

Antenna 70: 2459556

Antenna 70: 2459552

Antenna 70: 2459551

Antenna 70: 2459549

Antenna 70: 2459542

Antenna 70: 2459541

Antenna 70: 2459540

Antenna 70: 2459536

Antenna 70: 2459535

Antenna 70: 2459534

Antenna 70: 2459533

Antenna 70: 2459532

Antenna 70: 2459530

Antenna 70: 2459527

Antenna 70: 2459524

Antenna 70: 2459523

Antenna 70: 2459522

Antenna 70: 2459513

Antenna 70: 2459508

Antenna 70: 2459506

Antenna 70: 2459505

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶